Abstract

Info

Title:: End-to-end deep learning for auxiliary diagnosis of pneumonia using original lung sounds

Author(s):: XIAO Xin; GAO Yuqing; ZHANG Jianmin; School of Artificial Intelligence, Jianghan University, Wuhan 430056, China

Keywords:: Keywords: original lung sound pneumonia intelligent auxiliary diagnosis end-to-end learning DCL-Net

Abstract:: Abstract: An end-to-end auxiliary diagnosis method for pneumonia based on DCL-Net with dual-path of different convolutional kernels is proposed, in which no feature engineering is required, and the original lung sound signal is directly input into the model. The dual-path convolutional network with kernel sizes of 1*3 and 1*5, with each path containing 3 residual blocks, allows the model to automatically learn features of lung sounds at different scales while avoiding model degradation. The performance of the end-to-end method is validated through the comparisons with 3 commonly used feature extraction methods in signal analysis, namely Mel-spectrogram, short-time Fourier transform, and wavelet transform. The results show that the proposed method has a diagnostic accuracy of 61.4% for the 4-class classification task (normal, moderate, severe, critical), which is 1.6%, 5.0%, and 3.7% higher than the other 3 feature extraction methods, and the diagnostic accuracy is 89.7% for the binary classification task (normal or abnormal), which is 11.0%, 5.1%, and 11.2% higher than the other 3 feature engineering methods, demonstrating that it can serve as an effective diagnostic tool for pneumonia.